18 Learning and Expertise

Novice to Expert

One way to think about learning is in the context of progressing from a novice to an expert. Students generally start as novices in a topic, and, as educators, we try to move them to a greater level of expertise. Most students will not become experts, but we work to help them move along a continuum to gain proficiency.

How does expertise develop? “Expertise develops as learners accurately combine simple ideas into complex ones.” (Van Merrienboer & Sweller, 2010). This complexity is reflected in the schema held in our long-term memory (e.g. mental models, networks, circuits). For example, A toddler may call every animal a dog. A dog is a dog, a cat is a dog, a cow is a dog. The toddler has a very simple schema of what an animal and dog are – e.g. a non-human with four legs and fur. As the toddler gains experience, they begin to refine their schema for animals and types of animals. If they are paying close attention, they can distinguish dogs, cats, and cows.

However, it’s not enough just to build schema. “Well-designed instruction should not only encourage schema construction but should also support schema automation for those aspects that are consistent across tasks” (Van Merrienboer & Sweller, 2010). Expertise develops as learners accurately combine simple ideas into complex ones – and these schemata become automated. For example, at a glance, an expert can tell the breed of the dog and likely its age, physical health, and mental state. Automation is critical to expertise – to progress from knowing to doing, from performing once to performing regularly. Anders Ericsson is a researcher in the science of expertise. He studies how people become experts in various fields such as chess, music, and sports. Ericsson states that, “the key to improved mental performance of almost any sort is the development of mental structures that make it possible to avoid the limitations of short-term memory and deal effectively with large amounts of information at once” (Ericsson, 2016).

Figure 18.1. Schema automation is an important part of learning

All of us have limited slots available to us in working memory. How many? Around four slots. But an expert can process much more information at once. How do experts overcome the limitations of short-term memory? What are these mental structures Ericsson refers to? It is believed that even a highly complex schema can be a single element in working memory allowing us to process more information at once (Van Merrienboer & Sweller, 2010). “The amount of information that can be held depends strongly on whether the items can be grouped into meaningful units, or “chunks.” That is, by clustering information together one can exploit pre-existing information about concepts already stored in long-term memory, which allows more efficient storage in working memory, presumably by reducing the number of active elements that must be maintained in working memory” (Eriksson, Vogel, Lansner, Bergström, & Nyberg, 2015). Working memory slots are a conceptual simplification. Another way to think about it is that we have a limited number of cognitive resources and we distribute these resources among the things we pay attention to and are processing. If the schema are not automated they can use all the resources when activated leaving no resources for processing new information. The more the schema are automated, the fewer resources they use, and the greater the amount of resources available for any new information in front of us. “The opportunity to utilize either LTM or grouping tends to increase performance, while the requirement to report fine details of complex objects tends to decrease performance” (Eriksson et al., 2015). In short, we need to build schema, but we also need to support the automation (increased efficiency) of schema that we need to use often.

The following excerpts from Ericsson, Prietula and Cokely (2007) illustrate the interaction of schema, working memory and expert performance. “Working memory slots are similar among all of us. But the complexity of the schema we are able to apply to the situation in front of us will differ considerably – and this can largely predict performance.” The authors provide an example from the game of chess:

“A player explores all the possibilities for their next move, thinking through the consequences of each and planning out the sequence of moves that might follow. All of this requires working memory – the information to support these processes must be held in working memory. An expert has played so many times that they’ve seen the exact same or at least very similar pieces arrangements before. They’ve deliberated on the consequences of possible moves and can plan the subsequent moves and scenarios much further into the future (often to the end of the game) – enhancing their ability to make an effective choice for that specific move. For a novice, there is a lot of information to take in, starting with the position of the pieces, the rules of the game, the objectives – these place a limit on how far ahead the player can plan when thinking out their next move. More working memory resources are spent on information that an expert doesn’t have to think about. This greatly affects the ability to perform.”

Thus, learners not only have to make connections and make them more permanent, but also need to work toward automating the ones that will be frequently used. What often differentiates an expert from a novice is the number of automated tasks – freeing up more resources for new information.

References

Novice to Expert
  • Van Merriënboer, J. J., & Sweller, J. (2010). Cognitive load theory in health professional education: design principles and strategies. Medical education, 44(1), 85-93.
  • Ericsson, A., & Pool, R. (2016). Peak: Secrets from the new science of expertise. Houghton Mifflin Harcourt.
  • Eriksson, J., Vogel, E. K., Lansner, A., Bergström, F., & Nyberg, L. (2015). Neurocognitive architecture of working memory. Neuron, 88(1), 33-46.
  • Ericsson, K. A., Prietula, M. J., & Cokely, E. T. (2007 July-August). The Making of an Expert. Retrieved from https://hbr.org/2007/07/the-making-of-an-expert

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